To evaluate the potential of Varifocal and focal points outside fact-checking, we used the Amazon dataset of [1]}.
We trained our system (pretrained on SQuAD) on Amazon product descriptions only. Following Section REF , we extracted the focal points using SpaCy [2]}, trained a re-ranker, and then generated and ranked multiple questions per product.
We achieved a BLEU-4 score of 13.2 using only the focal points as guidance. In contrast to the reported experimental setup by [3]}, who used previously asked questions as part of the input, effectively predicting the missing question in a set, we generate all questions given the product descriptions only.
Their best model was trained on product descriptions, questions on the product, and previously asked questions about related products required to model missing information based on the notion of schema, i.e. global schema - local schema. This model achieves a BLEU-4 score of 18.55 and conditions BART on a usefulness classifier during decoding using Plug and Play Language Models (PPLMs) [4]}.
